Abstract
We propose and evaluate a systematic approach to detect and classify Patient/Problem, Intervention, Comparison and Outcome (PICO) from the medical literature. The training and test corpora were generated systematically and automatically from structured PubMed abstracts. 23,472 sentences by exact pattern match of head words of P-I-O categories. Afterward, the terms with top frequencies were used as the features of Naïve Bayesian classifier. This approach achieves F-measure values of 0.91 for Patient/Problem, 0.75 for Intervention and 0.88 for Outcome, comparable to previous studied based on mixed textural, paragraphical, and semantic features. In conclusion, we show that by stricter pattern matching criteria of training set, detection and classification of PICO elements can be reproducible with minimal expert intervention. The results of this work are higher than previous studies.
Original language | English |
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Title of host publication | Proceedings - 2011 IEEE International Conference on Granular Computing, GrC 2011 |
Pages | 279-283 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 2011 |
Event | 2011 IEEE International Conference on Granular Computing, GrC 2011 - Kaohsiung, Taiwan Duration: Nov 8 2011 → Nov 10 2011 |
Other
Other | 2011 IEEE International Conference on Granular Computing, GrC 2011 |
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Country/Territory | Taiwan |
City | Kaohsiung |
Period | 11/8/11 → 11/10/11 |
Keywords
- information extraction
- natural language processing
- question answering
- text mining
ASJC Scopus subject areas
- Software